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Today’s guest post/contest comes from Thomas McDermott, a frequent guest contributor. As always, we thank him for his hard work.


Author’s note regarding the numbers: The Win Probability (WP) numbers shown below were, for the most part, generated using a formula presented by Wayne Winston in his book Matheletics, and subsequently improved upon by Pro Football Reference for their WP model. [1]While in the process of messing around with PFR’s WP Calculator, I have noticed differences between the results from the Calculator and the results from using the formula as shown on their web … Continue reading

The heart of the formula is the Excel NORMDIST function, which returns the normal distribution (the probability) for a given mean and standard deviation. I have made some minor adjustments to this formula, but it is basically the same. The formula requires the use of Expected Points data; the EP dataset I use comes from Brian Burke’s Advanced Football Analytics site (when it was active), and I have adjusted those numbers for era. Since the formula falls apart in certain areas – most importantly, the 4th quarter when the game is close – I abandon it and use other data to generate a WP number. Field goal success rates, 4th down success rates, drive results, PFR’s Play Index and the recently provided play-by-play data from Ron Yurko on GitHub, are some of my alternate sources. I realize there’s a “black box” aspect to Win Probability analysis (if you can find two models that completely agree, let me know), since it’s not something that can be easily checked, and perhaps especially suspect when the author openly states that he adjusts the numbers “manually”. To that I can only say that my intent is to provide as accurate a picture as possible of the games that I analyze, and I’m open to any suggestions, comments or questions. Thanks. [continue reading…]

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References
1 While in the process of messing around with PFR’s WP Calculator, I have noticed differences between the results from the Calculator and the results from using the formula as shown on their web page. I have a hunch that PFR has probably moved on from the formula and is using a more sophisticated model (which perhaps incorporates the formula?), but I’m not sure.
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Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work. You can view all of his work at Football Perspective here.


If you can get five people in a room to agree on what a sports dynasty is, you’ll probably have achieved the most miraculous agreement in history since the Congress of Vienna. We know a sports dynasty when we see one (the current Patriots, the New York Yankees, 1990s Bulls, etc.), but it becomes less clear once we attempt to actually define it:,When does the dynasty start? How long must it last? What are the requirements?

In this article on NFL dynasties, FiveThirtyEight does a nice job of negotiating the quagmire by just listing the “best team over any number of years”. [1]Their definition of “best” being their ELO ratings. I’m going to do the same thing here, but focusing solely on NFL defenses since the merger (regular season only). The metric is points allowed by the defense (meaning: fumble, interception, kick and punt return touchdowns, and safeties aren’t included), adjusted for era and strength of schedule (basically, SRS ratings). Regular readers may recall that I published these results back in August 2015. To differentiate this stat from Pro-Football-Reference’s DSRS, I’ll call it “DfSRS”.

Below is a table of defensive dynasties, ranging from 1 to 15 years: [continue reading…]

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1 Their definition of “best” being their ELO ratings.
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Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work. You can view all of his work at Football Perspective here.


I wrote this article last year, when I generated the statistics and then ranked all starting quarterbacks in 2015 based on how well they played in “clutch” [1]Note that throughout this post, anything that happens within this situation is termed “clutch”; as in “clutch yards”, “clutch plays”, “clutch touchdown”, etc. situations. I used a simple definition: if it occurred in the 4th quarter or overtime, when the game was tied or the quarterback’s team was trailing by as much as one score (8 points), then it was a clutch situation.

The main metric used was Bryan Frye’s Total Adjusted Yards per Play, and today we’ll use the same methodology [2]In my post last year, I included a 2-point conversion bonus of 15 yards which I’m going to leave out for now. Besides not really adding much to the study, when I started collecting the data for the … Continue reading to find the 2016 Clutch Value Leader as well as the single season leaders since 1994. Here’s Bryan’s TAY/P formula, which Chase supports as an all-encompassing basic measure of quarterback performance:

(passing & rushing yards + (touchdowns * 20) – (interceptions * 45) – (fumbles lost * 25) – ( sack yards)) / (pass attempts + rush attempts + sacks) [3]Note that Bryan uses a 25-yard penalty for all fumbles (lost or recovered) while this study uses that penalty for lost fumbles only (which are the only ones being counted here).

The other change I’m making from the previous post, is that I’ll be using a 3-year rolling league average, as opposed to a single year league average, when adjusting for era. Thanks to Bryan (through his great website GridFe) for providing me with that information.

So let’s get to it. Below are the quarterbacks in 2016 who had at least 30 clutch action plays, [4]For 2015, I used 24 actions plays as the cutoff, after looking at the numbers more when doing the single-season rankings, 30 seemed more appropriate. and here’s how to read the table: [continue reading…]

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References
1 Note that throughout this post, anything that happens within this situation is termed “clutch”; as in “clutch yards”, “clutch plays”, “clutch touchdown”, etc.
2 In my post last year, I included a 2-point conversion bonus of 15 yards which I’m going to leave out for now. Besides not really adding much to the study, when I started collecting the data for the single season and career leaders in this metric, I found that the data on 2-point conversions is somewhat spotty before 2005; in fact, in most cases before 1998, the players involved aren’t even mentioned. So, for those of you who read the last post, taking away that conversion bonus means Eli Manning is at the top for 2015 and not Jay Cutler.
3 Note that Bryan uses a 25-yard penalty for all fumbles (lost or recovered) while this study uses that penalty for lost fumbles only (which are the only ones being counted here).
4 For 2015, I used 24 actions plays as the cutoff, after looking at the numbers more when doing the single-season rankings, 30 seemed more appropriate.
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Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work. You can read all of his guest posts at Football Perspective at this link.


The following is a bunch of data I’ve gathered regarding home-field advantage; hopefully some of you will find it useful for analysis, or for picking winners against the spread in your pick’em games this year!

The general consensus is that the home team in a typical NFL game has an advantage of around 2.5 to 3 points, and this is right on: since 1970, the average team wins their regular season home games [1]The HFA number during the playoffs over that same period is 6.5, but that’s probably due to playoff seeding than fan/stadium involvement; it might be interesting to look into this further. by 2.7 points, [2]As far as what causes home teams to have an advantage at home, Brian Burke suggests in this article that it has more to do with environmental familiarity, and other factors, than the effect of … Continue reading with a high of 4.6 in 1985 and a low of 0.8 in 2006. If we do a linear regression, we can see that HFA appears to be in decline, but only slightly compared to points per game, which is obviously increasing:

pts hfa [continue reading…]

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1 The HFA number during the playoffs over that same period is 6.5, but that’s probably due to playoff seeding than fan/stadium involvement; it might be interesting to look into this further.
2 As far as what causes home teams to have an advantage at home, Brian Burke suggests in this article that it has more to do with environmental familiarity, and other factors, than the effect of screaming fans.
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Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work.


One way we can tell if a quarterback is “clutch” – meaning, he plays well when he absolutely has to – is by looking at his 4th Quarter Comebacks (4QC) and Game Winning Drives (GWD). Below are definitions for both [1]As many of you already know, Scott Kacsmar did a ton of great work in this area; his “Quarterbacks and fourth quarter comebacks” article at the old PFR Blog provides all the information you’ll … Continue reading:

    4th Quarter Comeback: In the 4th quarter, the quarterback leads a scoring drive while being down one score or less that results in the game being tied or his team taking the lead. As long as the QB’s team wins the game, the QB gets credit for the 4QC, even if his scoring drive wasn’t the game-winning drive.

    Game-Winning Drive: In the 4th quarter or overtime, the quarterback leads a scoring drive that results in his team taking the lead (meaning, breaking a tie or overcoming a deficit) for the last time.

[continue reading…]

References

References
1 As many of you already know, Scott Kacsmar did a ton of great work in this area; his “Quarterbacks and fourth quarter comebacks” article at the old PFR Blog provides all the information you’ll ever need about 4QC/GWDs.
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Data Dump: Defensive Points Allowed SRS

Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work.


In a previous post, I provided SRS-style ratings for all offenses since 1970, using only points scored by the actual offense (including field goals). Today, I’ll do the same thing for defenses – meaning, of course, our “metric” will be points allowed only by the actual defense. [1]To quickly recap: SRS ratings for offense (OSRS) and defense (DSRS) on PFR’s website include points scored by the defense and special teams. To get a more accurate points-based evaluation of … Continue reading

Here’s how to read the table below: in 1970, the Vikings allowed 10.2 points per game, 8.2 of which came from touchdowns and field goals allowed by the defense. This leaves 2.0 PPG scored by their opponent’s defense or special teams (i.e., due to Minnesota’s offense or special teams). [2]In this case, it was the result of three touchdowns off of offensive turnovers and one on special teams, as highlighted by Chase in this post on estimated points allowed per drive. Their 8.2 Def PA/G was 9.5 points better than league average; after adjusting for strength of opponent, their rating remains at 9.5. Their overall points allowed SRS rating (DSRS) is 9.2, meaning PFR’s defensive SRS rating undersells them by 0.3 points. [continue reading…]

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1 To quickly recap: SRS ratings for offense (OSRS) and defense (DSRS) on PFR’s website include points scored by the defense and special teams. To get a more accurate points-based evaluation of offenses and defenses, I weeded these scores out and reran the iterations. I didn’t note this last time, but for those interested: the numbers used do not include any home field advantage adjustment or a cap on blowout point differentials.
2 In this case, it was the result of three touchdowns off of offensive turnovers and one on special teams, as highlighted by Chase in this post on estimated points allowed per drive.
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Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work.


When looking at teams’ offensive SRS ratings (OSRS) on PFR, we know that those number also include points scored by the defense and special teams – punt and kick return touchdowns, interception and fumble return touchdowns, return scores on blocked punts and field goals, and safeties. This makes OSRS not as accurate a point-based rating of the offense “proper” as it could be. But, considering those “non-offense” types of scores make up a small fraction of a team’s overall points scored in a season (the average is around 8% since 1970), we can generally ignore this “hiccup” in the system.

Well, most of us can ignore it; for some reason, I cannot! My curiosity has gotten the better of me, so I decided to run offensive and defensive SRS ratings for each team since the merger, using only points that we would normally credit the offense for scoring (or the defense for allowing) – passing and rushing touchdowns, and field goals. [1]I have to assume that at some point Chase or one of the guys at PFR has run the numbers for “SRS without special teams/defense scores”, but I have yet to find it.

As the title states, this is a data dump; I’m hoping that readers of this site will find the info useful for their own research or general interest. Today, we’ll just look at the offense, I’ll post the numbers for defense in a follow-up post. [continue reading…]

References

References
1 I have to assume that at some point Chase or one of the guys at PFR has run the numbers for “SRS without special teams/defense scores”, but I have yet to find it.
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